import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_  : Any								=					logging.get_logger(__name__)

lowercase_  : Optional[int]								=					{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   "mvp"
    __a						=   ["past_key_values"]
    __a						=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
    def __init__(	self			,			lowerCAmelCase=50267			,			lowerCAmelCase=1024			,			lowerCAmelCase=12			,			lowerCAmelCase=4096			,			lowerCAmelCase=16			,			lowerCAmelCase=12			,			lowerCAmelCase=4096			,			lowerCAmelCase=16			,			lowerCAmelCase=0.0			,			lowerCAmelCase=0.0			,			lowerCAmelCase="gelu"			,			lowerCAmelCase=1024			,			lowerCAmelCase=0.1			,			lowerCAmelCase=0.0			,			lowerCAmelCase=0.0			,			lowerCAmelCase=0.02			,			lowerCAmelCase=0.0			,			lowerCAmelCase=False			,			lowerCAmelCase=True			,			lowerCAmelCase=1			,			lowerCAmelCase=0			,			lowerCAmelCase=2			,			lowerCAmelCase=True			,			lowerCAmelCase=2			,			lowerCAmelCase=2			,			lowerCAmelCase=False			,			lowerCAmelCase=100			,			lowerCAmelCase=800			,			**lowerCAmelCase			,			)							->       Dict:
     SCREAMING_SNAKE_CASE__: List[Any]=   vocab_size
     SCREAMING_SNAKE_CASE__: Optional[int]=   max_position_embeddings
     SCREAMING_SNAKE_CASE__: int=   d_model
     SCREAMING_SNAKE_CASE__: Optional[Any]=   encoder_ffn_dim
     SCREAMING_SNAKE_CASE__: Dict=   encoder_layers
     SCREAMING_SNAKE_CASE__: Optional[Any]=   encoder_attention_heads
     SCREAMING_SNAKE_CASE__: List[str]=   decoder_ffn_dim
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   decoder_layers
     SCREAMING_SNAKE_CASE__: Dict=   decoder_attention_heads
     SCREAMING_SNAKE_CASE__: Any=   dropout
     SCREAMING_SNAKE_CASE__: str=   attention_dropout
     SCREAMING_SNAKE_CASE__: List[Any]=   activation_dropout
     SCREAMING_SNAKE_CASE__: Optional[int]=   activation_function
     SCREAMING_SNAKE_CASE__: Optional[int]=   init_std
     SCREAMING_SNAKE_CASE__: List[str]=   encoder_layerdrop
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   decoder_layerdrop
     SCREAMING_SNAKE_CASE__: List[Any]=   classifier_dropout
     SCREAMING_SNAKE_CASE__: List[Any]=   use_cache
     SCREAMING_SNAKE_CASE__: Dict=   encoder_layers
     SCREAMING_SNAKE_CASE__: List[Any]=   scale_embedding  # scale factor will be sqrt(d_model) if True
     SCREAMING_SNAKE_CASE__: int=   use_prompt
     SCREAMING_SNAKE_CASE__: List[Any]=   prompt_length
     SCREAMING_SNAKE_CASE__: str=   prompt_mid_dim

     super().__init__(
         pad_token_id=lowerCAmelCase			,			bos_token_id=lowerCAmelCase			,			eos_token_id=lowerCAmelCase			,			is_encoder_decoder=lowerCAmelCase			,			decoder_start_token_id=lowerCAmelCase			,			forced_eos_token_id=lowerCAmelCase			,			**lowerCAmelCase			,			)

     if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''			,			lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.bos_token_id
      warnings.warn(
          f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
          '''The config can simply be saved and uploaded again to be fixed.''' )







'''simple docstring'''


from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

UpperCAmelCase_					=   {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

UpperCAmelCase_					=   {
    "facebook/blenderbot_small-90M": 5_12,
}





class  __lowercase    (	__magic_name__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   PRETRAINED_VOCAB_FILES_MAP
   _a    						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a    						=   BlenderbotSmallTokenizer







   def __init__(						self   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase=False   ,				UpperCamelCase=True   ,				**UpperCamelCase   ,				)					->     List[Any]:
     super().__init__(
         ByteLevelBPETokenizer(
             vocab=UpperCamelCase   ,				merges=UpperCamelCase   ,				add_prefix_space=UpperCamelCase   ,				trim_offsets=UpperCamelCase   ,				)   ,				bos_token=UpperCamelCase   ,				eos_token=UpperCamelCase   ,				unk_token=UpperCamelCase   ,				**UpperCamelCase   ,				)
     __a								=       add_prefix_space







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=None   )					->     List[Any]:
     __a								=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
       return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     List[int]:
     __a								=       [self.sep_token_id]
     __a								=       [self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep   ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]



